Abstract
This paper presents a novel design for the kinematic control structure of the wheeled mobile robot (WMR) path planning and path-following. The proposed system is focused on the implementation of practical real-time model-free algorithms based on visual servoing. The mainframe of this study is to implement a novel kinematic control structure based on visual sevoing and hybrid algorithms in real-time mobile robot applications. First, the structure of the proposed algorithm based on the visual information extracted from an overhead camera has been addressed. Then, the classification process of robot position and orientation, target, and obstacles has been addressed. Second, the path planning algorithms’ initial parameters and obstacles-free path coordinates have been determined by visual information extracted from images in real time. In this step, the interval type-2 fuzzy inference (IT2FIS) algorithm and various algorithms used in path planning have been compared and their performances have been analyzed. The third stage handled the path-following process using a novel control structure for keeping up the robot on the generated path. In this step, the proposed approach is compared with fuzzy Type-1/Type-2 and fuzzy-PID control algorithms, and their results have been analyzed statistically. The proposed system has been successfully implemented on several maps. The experimental results show that the developed design is valid in generating collision-free paths efficiently and consistently and able to guide the robot to follow the path in real time.
Similar content being viewed by others
References
Umoh, U., Udoh, S., Isong, E., Asuquo, R.: PSO Optimized interval Type-2 Fuzzy Design for Elections Results Prediction. Int. J. Log. Syst. (2019). https://doi.org/10.5121/ijfls.2019.9101
Elsheikh, E.A., El-Bardini, M.A., Fkirin, M.A.: Practical design of a path following for a non-holonomic mobile robot based on a decentralized fuzzy logic controller and multiple cameras. Arab J. Sci. Eng. 41, 3215–3229 (2016)
Souissi O, et al., Path planning: A 2013 survey numerical analysis view project scheduling under resources and temporal constraints view project path planning: a 2013 survey, (2013)
Dönmez, E., Kocamaz, A.F., Dirik, M.: A vision-based real-time mobile robot controller design based on Gaussian Function for indoor environment. Arab. J. Sci. Eng. 43, 1–16 (2017)
Cherroun, L., Boumehraz, M., Kouzou, A.: Mobile robot path planning based on optimized fuzzy logic controllers. Springer, Singapore (2019)
Jhang, J.-Y., Lin, C.-J., Lin, C.-T., Young, K.-Y.: Navigation control of mobile robots using an interval type-2 fuzzy controller based on dynamic-group particle Swarm Optimization. Int. J. Control Autom. Syst. 16(5), 2446–2457 (2018)
Han, J., Seo, Y.: Mobile robot path planning with surrounding point set and path improvement. Appl. Soft Comput. J. 57, 35–47 (2017)
Patle, B.K., Parhi, D.R.K., Jagadeesh, A., Kashyap, S.K.: Application of probability to enhance the performance of fuzzy-based mobile robot navigation. Appl. Soft Comput. 75, 265–283 (2018)
Kala, R., Shukla, A., Tiwari, R.: Robotic path planning in static environment using hierarchical multi-neuron heuristic search and probability-based fitness. Neurocomputing 74(14–15), 2314–2335 (2011)
Duchon, F., et al.: Path planning with modified A star algorithm for a mobile robot. Proced. Eng. 96, 59–69 (2014)
Moreno, L., Armingol, J.M., Garrido, S., La Escalera, A., Salichs, M.A.: A genetic algorithm for mobile robot localization using ultrasonic sensors. J. Intell. Robot. Syst. Theory Appl. 34(2), 135–154 (2002)
S. A. Fadzli, S. I. Abdulkadir, M. Makhtar, and A. A. Jamal, “Robotic Indoor Path Planning using Dijkstra’ s Algorithm with Multi-Layer Dictionaries,” pp. 1–4, 2015. https://doi.org/10.1109/ICISSEC.2015.7371031
Kavraki, L.E., Švestka, P., Latombe, J.C., Overmars, M.H.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Robot Autom. 12(4), 566–580 (1996)
Naderi, K., Rajamäki, J., Hämäläinen P., RT-RRT*: a real-time path planning algorithm based on RRT*.” (2015). https://doi.org/10.1145/2822013.2822036
Bruce, J., Veloso, M.: Real-time randomized path planning for robot navigation. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 3, 2383–2388 (2002)
Vinet L., Zhedanov A.: Rapidly-exploring random trees: a new tool for path planning. J. Phys. A Math. Theor., 2011. http://msl.cs.illinois.edu/~lavalle/papers/Lav98c.pdf
Dönmez E., Kocamaz A.F., Dirik M.: “Bi-RRT path extraction and curve-fitting smooth with visual based configuration space mapping,” in IDAP 2017—International Artificial Intelligence and Data Processing Symposium. Malatya, Turkey (2017). https://doi.org/10.1109/IDAP.2017.8090214
Weerakoon, T., Ishii, K., Nassiraei, A.A.F.: An artificial potential field based mobile robot navigation method to prevent from deadlock. J. Artif. Intell. Soft. Comput. Res 5(3), 189–203 (2015)
Martínez, R., Castillo, O., Aguilar, L.T.: Intelligent control for a perturbed autonomous wheeled mobile robot using type-2 fuzzy logic and genetic algorithms. J. Autom. Mob. Robot. Intell. Syst. 2, 12–22 (2008)
Abiyev, R.H., Erin, B., Denker, A.: Navigation of mobile robot using type-2 fuzzy system. In: Huang, D.-S., Hussain, A., Han, K., Gromiha, M.M. (eds.) Intelligent computing methodologies, vol. 10363, pp. 15–26. Springer International Publishing, Cham (2017)
Liao, T.W.: A procedure for the generation of interval type-2 membership functions from data. Appl. Soft Comput. J. 52, 925–936 (2017)
Ider, M.: Type-2 fuzzy logic control for a mobile robot tracking a moving target. MJMS. 3, 57–65 (2015)
Srinivasan K., Gu J.: Multiple sensor fusion in mobile robot localization, 2007 Can. Conf. Electr. Comput. Eng., 1207–1210 (2007)
Almasri, M., Elleithy, K., Alajlan, A.: Sensor fusion based model for collision free mobile robot navigation. Sensors 16(1), 24 (2016)
ShitsukaneA., Cheruiyot W., Otieno C., Mvurya M.: Fuzzy logic sensor fusion for obstacle avoidance mobile robot,” 2018 IST-Africa Week Conf., no. May, p. Page 1 of 8-Page 8 of 8, 2018
Fu W.:Visual servoing for mobile robots navigation with collision avoidance and field-of-view constraints To cite this version: Intégrative et Systèmes Complexes Visual Servoing for Mobile Robots Navigation with Collision Avoidance and Field-of-View Constraint,” 2016. https://www.researchgate.net/publication/325333817_Fuzzy_Logic_Sensor_Fusion_for_Obstacle_Avoidance_Mobile_Robot
Aye YY.: Design of an image-based fuzzy controller for parking problems of a car-like mobile robot,” no. March, 2017. http://eprints.lib.okayama-u.ac.jp/files/public/5/55101/20170524144712242504/K0005548_fulltext.pdf
Ziaei, Z., Oftadeh, R., Mattila, J.: Vision-based path coordination for multiple mobile robots with four steering wheels using an overhead camera. IEEE/ASME Int Conf Adv Intell Mechatronics 2015, 261–268 (2015)
Elsheikh, E.A., El-Bardini, M.A., Fkirin, M.A.: Practical design of a path following for a non-holonomic mobile robot based on a decentralized fuzzy logic controller and multiple cameras. Arab. J. Sci. Eng. 41(8), 3215–3229 (2016)
XieJ., Nashashibi F., Parent M., Favrot OG., A real-time robust global localization for autonomous mobile robots in large environments, In: 11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010, 2010.
Baklouti, N., John, R., Alimi, A.: Interval type-2 fuzzy logic control of mobile robots. J. Intell. Learn. Syst. Appl. 04(November), 291–302 (2012)
Wang, M., Liu, J.N.K.: Fuzzy logic-based real-time robot navigation in an unknown environment with dead ends. Rob. Auton. Syst. 56(7), 625–643 (2008)
Omrane, H., Masmoudi, M.S., Masmoudi, M.: Fuzzy logic based control for autonomous mobile. Comput. Intell. Neurosci. 2016, 1–10 (2016)
Liang, Q., Mendel, J.: Interval type-2 fuzzy logic systems: theory and design. IEEE Trans. Fuzzy Syst. 8(5), 535–550 (2000)
Castillo O., Melin P., Kacprzyk J., Pedrycz W.: Type-2 fuzzy logic: theory and applications, In: 2007 IEEE International Conference on Granular Computing (GRC 2007), 145–145 (2007)
Castillo, O.: Type-2 fuzzy logic in intelligent control applications. Springer, Heidelberg (2012)
D’Andrea, A., Pellegrino, O.: Application of fuzzy techniques for determining the operating speed based on road geometry. PROMET Traffic Transp 24(3), 203–214 (2012)
Brcko, T., Svetak, J.: Fuzzy Reasoning as a Base for collision avoidance decision support system. PROMET Traffic Transp 25, 555–564 (2013)
Castillo, O.: Interval type-2 fuzzy logic for hybrid intelligent control. Studies in fuzziness and soft computing 298, 91–94 (2013)
Kwon, K.-S., Ready, S.: Practical guide to machine vision software: an introduction with LabVIEW. Wiley, Hoboken (2014)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Dirik, M., Kocamaz, A.F. & Castillo, O. Global Path Planning and Path-Following for Wheeled Mobile Robot Using a Novel Control Structure Based on a Vision Sensor. Int. J. Fuzzy Syst. 22, 1880–1891 (2020). https://doi.org/10.1007/s40815-020-00888-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40815-020-00888-9